{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "from pathlib import Path\n",
    "workdir = Path(\"./coeqtl_mapping/\")\n",
    "\n",
    "celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']\n",
    "import matplotlib\n",
    "def heatmap(data, row_labels, col_labels, ax=None,\n",
    "            cbar_kw={}, cbarlabel=\"\", **kwargs):\n",
    "    \"\"\"\n",
    "    Create a heatmap from a numpy array and two lists of labels.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    data\n",
    "        A 2D numpy array of shape (M, N).\n",
    "    row_labels\n",
    "        A list or array of length M with the labels for the rows.\n",
    "    col_labels\n",
    "        A list or array of length N with the labels for the columns.\n",
    "    ax\n",
    "        A `matplotlib.axes.Axes` instance to which the heatmap is plotted.  If\n",
    "        not provided, use current axes or create a new one.  Optional.\n",
    "    cbar_kw\n",
    "        A dictionary with arguments to `matplotlib.Figure.colorbar`.  Optional.\n",
    "    cbarlabel\n",
    "        The label for the colorbar.  Optional.\n",
    "    **kwargs\n",
    "        All other arguments are forwarded to `imshow`.\n",
    "    \"\"\"\n",
    "\n",
    "    if not ax:\n",
    "        ax = plt.gca()\n",
    "\n",
    "    # Plot the heatmap\n",
    "    im = ax.pcolormesh(data, **kwargs)\n",
    "\n",
    "    # Create colorbar\n",
    "    cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n",
    "    cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n",
    "\n",
    "    # Let the horizontal axes labeling appear on top.\n",
    "    ax.tick_params(top=True, bottom=False,\n",
    "                   labeltop=True, labelbottom=False)\n",
    "\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "\n",
    "    # Turn spines off and create white grid.\n",
    "#     ax.spines[:].set_visible(False)\n",
    "\n",
    "#     ax.set_xticks(np.arange(-0.5, data.shape[1]-2, 1), minor=True)\n",
    "#     ax.set_yticks(np.arange(-0.5, data.shape[0]-2, 1), minor=True)\n",
    "    # Show all ticks and label them with the respective list entries.\n",
    "    ax.set_xticklabels([\"\"]+col_labels)\n",
    "    ax.set_yticklabels([\"\"]+row_labels)\n",
    "#     ax.grid(which='minor', color=\"white\", linestyle='-', linewidth=2)\n",
    "#     ax.tick_params(which=\"minor\", bottom=False, left=False)\n",
    "    return im, cbar\n",
    "\n",
    "\n",
    "def annotate_heatmap(im, data=None, valfmt=\"{x:.2f}\",\n",
    "                     textcolors=(\"black\", \"white\"),\n",
    "                     threshold=None, **textkw):\n",
    "    \"\"\"\n",
    "    A function to annotate a heatmap.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    im\n",
    "        The AxesImage to be labeled.\n",
    "    data\n",
    "        Data used to annotate.  If None, the image's data is used.  Optional.\n",
    "    valfmt\n",
    "        The format of the annotations inside the heatmap.  This should either\n",
    "        use the string format method, e.g. \"$ {x:.2f}\", or be a\n",
    "        `matplotlib.ticker.Formatter`.  Optional.\n",
    "    textcolors\n",
    "        A pair of colors.  The first is used for values below a threshold,\n",
    "        the second for those above.  Optional.\n",
    "    threshold\n",
    "        Value in data units according to which the colors from textcolors are\n",
    "        applied.  If None (the default) uses the middle of the colormap as\n",
    "        separation.  Optional.\n",
    "    **kwargs\n",
    "        All other arguments are forwarded to each call to `text` used to create\n",
    "        the text labels.\n",
    "    \"\"\"\n",
    "\n",
    "    # Normalize the threshold to the images color range.\n",
    "    if threshold is not None:\n",
    "        threshold = im.norm(threshold)\n",
    "    else:\n",
    "        threshold = im.norm(data.max())/2.\n",
    "\n",
    "    # Set default alignment to center, but allow it to be\n",
    "    # overwritten by textkw.\n",
    "    kw = dict(horizontalalignment=\"center\",\n",
    "              verticalalignment=\"center\")\n",
    "    kw.update(textkw)\n",
    "\n",
    "    # Get the formatter in case a string is supplied\n",
    "    if isinstance(valfmt, str):\n",
    "        valfmt = matplotlib.ticker.StrMethodFormatter(valfmt)\n",
    "\n",
    "    # Loop over the data and create a `Text` for each \"pixel\".\n",
    "    # Change the text's color depending on the data.\n",
    "    texts = []\n",
    "    for i in range(data.shape[0]):\n",
    "        for j in range(data.shape[1]):\n",
    "#             kw.update(color=textcolors[int(im.norm(data[i, j]) > threshold)])\n",
    "            text = im.axes.text(j+0.5, i+0.5, valfmt(data[i, j], None), **kw)#j+0.1, i+0.5\n",
    "            texts.append(text)\n",
    "\n",
    "    return texts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## celltypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_res_df = pd.read_csv(workdir/'output/filtered_results/rb_calculations/summary.csv', index_col=0)\n",
    "unfiltered_res_df = pd.read_csv(workdir/'output/unfiltered_results/rb_calculations/summary.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_res_df_clean = filtered_res_df[filtered_res_df['celltype_discovery']!='B']\n",
    "filtered_res_df_clean = filtered_res_df_clean.dropna()\n",
    "filtered_res_df_clean.to_excel(workdir/'output/summary/rb_values_replication_in_other_celltypes_filtered_results.xlsx')\n",
    "\n",
    "unfiltered_res_df_clean = unfiltered_res_df[unfiltered_res_df['celltype_discovery']!='B']\n",
    "unfiltered_res_df_clean = unfiltered_res_df_clean.dropna()\n",
    "unfiltered_res_df_clean.to_excel(workdir/'output/summary/rb_values_replication_in_other_celltypes_unfiltered_results.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### filtered results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# filtered results\n",
    "rb_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
    "                     columns=celltypes, index=celltypes)\n",
    "rbse_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
    "                     columns=celltypes, index=celltypes)\n",
    "rbpvalue_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
    "                     columns=celltypes, index=celltypes)\n",
    "numcoeqtl_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
    "                     columns=celltypes, index=celltypes)\n",
    "num_anno_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
    "                     columns=celltypes, index=celltypes)\n",
    "rbse_anno_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
    "                     columns=celltypes, index=celltypes)\n",
    "for discovery_celltype in celltypes:\n",
    "    # replication in other celltypes\n",
    "    for replication_celltype in celltypes:\n",
    "        if discovery_celltype != replication_celltype:\n",
    "            rb_results = filtered_res_df[(filtered_res_df['celltype_discovery'] == discovery_celltype) &\n",
    "                                         (filtered_res_df['celltype_replication'] == replication_celltype)]\n",
    "            replicated_coeqtls_num = pd.read_csv(\n",
    "                workdir/f'output/filtered_results/rb_calculations/discovery_{discovery_celltype}_replication_{replication_celltype}.tsv.gz',\n",
    "                compression='gzip',\n",
    "                sep='\\t',\n",
    "                index_col=0\n",
    "            ).shape[0]\n",
    "            if rb_results['r'].values[0] < 10 and discovery_celltype != 'B':\n",
    "                rb_df.loc[replication_celltype, discovery_celltype] = rb_results['r'].values[0]\n",
    "                rbse_df.loc[replication_celltype, discovery_celltype] = rb_results['se_r'].values[0]\n",
    "                rbpvalue_df.loc[replication_celltype, discovery_celltype] = rb_results['p'].values[0]\n",
    "                numcoeqtl_df.loc[replication_celltype, discovery_celltype] = replicated_coeqtls_num\n",
    "                rbvalue = rb_results['r'].values[0]\n",
    "                rbsevalue = rb_results['se_r'].values[0]\n",
    "                num_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"N={replicated_coeqtls_num}\"\n",
    "                rbse_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"{rbvalue:.2f}\\nN={replicated_coeqtls_num}\"\n",
    "            elif discovery_celltype == 'B':\n",
    "                rb_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
    "                rbse_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
    "                rbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
    "                numcoeqtl_df.loc[replication_celltype, discovery_celltype] = replicated_coeqtls_num\n",
    "                rbvalue = rb_results['r'].values[0]\n",
    "                rbsevalue = rb_results['se_r'].values[0]\n",
    "                num_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"N={replicated_coeqtls_num}\"\n",
    "                rbse_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"N={replicated_coeqtls_num}\"\n",
    "            else:\n",
    "                rb_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
    "                rbse_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
    "                rbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
    "                numcoeqtl_df.loc[replication_celltype, discovery_celltype] = replicated_coeqtls_num\n",
    "                num_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"N={replicated_coeqtls_num}\"\n",
    "                rbse_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"N={replicated_coeqtls_num}\"\n",
    "        else:\n",
    "            rb_df.loc[replication_celltype, discovery_celltype] = 1\n",
    "            rbse_df.loc[replication_celltype, discovery_celltype] = 0\n",
    "            rbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
    "            replicated_coeqtls_num = pd.read_csv(\n",
    "                workdir/f'output/filtered_results/UT_{discovery_celltype}/coeqtls_fullresults_fixed.sig.tsv.gz',\n",
    "                compression='gzip',\n",
    "                sep='\\t'\n",
    "            ).shape[0]\n",
    "            numcoeqtl_df.loc[replication_celltype, discovery_celltype] = replicated_coeqtls_num\n",
    "            num_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"N={replicated_coeqtls_num}\"\n",
    "            rbse_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"N={replicated_coeqtls_num}\"\n",
    "    \n",
    "replicated_ratio_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
    "                                   columns=celltypes, index=celltypes)\n",
    "for discovery_celltype in numcoeqtl_df.columns:\n",
    "    for replication_celltype in numcoeqtl_df.index:\n",
    "        replicated_ratio_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "        numcoeqtl_df.loc[replication_celltype, discovery_celltype] / numcoeqtl_df.loc[discovery_celltype, discovery_celltype]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CD4T</th>\n",
       "      <th>CD8T</th>\n",
       "      <th>monocyte</th>\n",
       "      <th>DC</th>\n",
       "      <th>NK</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CD4T</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.971596</td>\n",
       "      <td>0.759425</td>\n",
       "      <td>0.773429</td>\n",
       "      <td>0.953264</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD8T</th>\n",
       "      <td>0.988285</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.847118</td>\n",
       "      <td>1.002450</td>\n",
       "      <td>0.966100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monocyte</th>\n",
       "      <td>0.792142</td>\n",
       "      <td>0.779688</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.797139</td>\n",
       "      <td>0.960618</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>0.794745</td>\n",
       "      <td>0.815816</td>\n",
       "      <td>0.935905</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.853924</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NK</th>\n",
       "      <td>0.925802</td>\n",
       "      <td>0.967842</td>\n",
       "      <td>0.868747</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.918479</td>\n",
       "      <td>0.952496</td>\n",
       "      <td>0.948709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              CD4T      CD8T  monocyte        DC        NK    B\n",
       "CD4T      1.000000  0.971596  0.759425  0.773429  0.953264  NaN\n",
       "CD8T      0.988285  1.000000  0.847118  1.002450  0.966100  NaN\n",
       "monocyte  0.792142  0.779688  1.000000  0.797139  0.960618  NaN\n",
       "DC        0.794745  0.815816  0.935905  1.000000  0.853924  NaN\n",
       "NK        0.925802  0.967842  0.868747       NaN  1.000000  NaN\n",
       "B         0.918479  0.952496  0.948709       NaN       NaN  1.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rb_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CD4T</th>\n",
       "      <th>CD8T</th>\n",
       "      <th>monocyte</th>\n",
       "      <th>DC</th>\n",
       "      <th>NK</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CD4T</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.126679e-35</td>\n",
       "      <td>2.425843e-03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD8T</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.557685e-59</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monocyte</th>\n",
       "      <td>1.052643e-121</td>\n",
       "      <td>5.216640e-92</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.774726e-21</td>\n",
       "      <td>1.393096e-317</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>3.609987e-25</td>\n",
       "      <td>4.217830e-39</td>\n",
       "      <td>5.947381e-316</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.322965e-05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NK</th>\n",
       "      <td>2.552726e-264</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.365584e-06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2.320757e-144</td>\n",
       "      <td>1.610287e-212</td>\n",
       "      <td>1.074123e-78</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   CD4T           CD8T       monocyte            DC  \\\n",
       "CD4T       0.000000e+00   0.000000e+00   1.126679e-35  2.425843e-03   \n",
       "CD8T       0.000000e+00   0.000000e+00   7.557685e-59  0.000000e+00   \n",
       "monocyte  1.052643e-121   5.216640e-92   0.000000e+00  1.774726e-21   \n",
       "DC         3.609987e-25   4.217830e-39  5.947381e-316  0.000000e+00   \n",
       "NK        2.552726e-264   0.000000e+00   8.365584e-06  0.000000e+00   \n",
       "B         2.320757e-144  1.610287e-212   1.074123e-78  0.000000e+00   \n",
       "\n",
       "                     NK    B  \n",
       "CD4T       0.000000e+00  0.0  \n",
       "CD8T       0.000000e+00  0.0  \n",
       "monocyte  1.393096e-317  0.0  \n",
       "DC         4.322965e-05  0.0  \n",
       "NK         0.000000e+00  0.0  \n",
       "B          0.000000e+00  0.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbpvalue_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_dict = {'CD4T': '#2E9D33',\n",
    "              'CD8T': '#126725',\n",
    "              'monocyte': '#EDBA1B',\n",
    "              'NK': '#965EC8',\n",
    "              'DC': '#E64B50',\n",
    "              'B': '#009DDB',\n",
    "              'cMono': 'peru',\n",
    "              'ncMono': 'y',\n",
    "              'CD4T_individual_100': '#2E9D33',\n",
    "              'CD4T_individual_50': '#2E9D33',\n",
    "              'CD4T_50': '#2E9D33',\n",
    "              'CD4T_150': '#2E9D33',\n",
    "              'CD4T_250': '#2E9D33'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-1-1162ad6d26ab>:60: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([\"\"]+col_labels)\n",
      "<ipython-input-1-1162ad6d26ab>:61: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels([\"\"]+row_labels)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 16})\n",
    "discovery_celltype = 'CD4T'\n",
    "fig, axes = plt.subplots(1, 6, figsize=(7, 7), sharey=True)\n",
    "for i, discovery_celltype in enumerate(['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']):\n",
    "    colors = [\"white\", color_dict[discovery_celltype]]\n",
    "    cmap1 = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
    "    im1, bar = heatmap(rb_df[discovery_celltype].values.reshape((6, 1)), \n",
    "            list(rb_df.index), \n",
    "            [discovery_celltype],\n",
    "                     cmap=cmap1, ax=axes[i], vmin=0.7, vmax=1)\n",
    "    bar.remove()\n",
    "    _ = annotate_heatmap(im1, \n",
    "                         data=rbse_anno_df[discovery_celltype].values.reshape((6, 1)), \n",
    "                         valfmt=\"{x:^}\", \n",
    "                         textcolors=(\"white\", \"white\"),\n",
    "                         threshold=1)\n",
    "    if i > 0:\n",
    "        axes[i].axis('off')\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "plt.savefig('rb_values.filtered_results.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cdict = {'red':   [[0.0,  0.0, 0.0],\n",
    "#                    [0.5,  0.5, 0.5],\n",
    "#                    [1.0,  1.0, 1.0]],\n",
    "         \n",
    "#          'green': [[0.0,  0.0, 0.0],\n",
    "#                    [0.5, 0.5, 0.5],\n",
    "#                    [1.0,  1.0, 1.0]],\n",
    "         \n",
    "#          'blue':  [[0.0,  0.0, 0.0],\n",
    "#                    [0.5,  0.5, 0.5],\n",
    "#                    [1.0,  1.0, 1.0]]}\n",
    "\n",
    "# cdict['alpha'] = ((0.0, 0.0, 0.0),\n",
    "#                    (0.5, 0.5, 0.5),\n",
    "#                    (1.0, 1.0, 1.0))\n",
    "# newcmp = LinearSegmentedColormap('alpha', segmentdata=cdict, N=256)\n",
    "\n",
    "c_white = matplotlib.colors.colorConverter.to_rgba('white',alpha = 0)\n",
    "c_black= matplotlib.colors.colorConverter.to_rgba('black',alpha = 1)\n",
    "cmap_rb = matplotlib.colors.LinearSegmentedColormap.from_list('rb_cmap',[c_white,c_black],512)\n",
    "\n",
    "\n",
    "\n",
    "mpl.cm.register_cmap(cmap=cmap_rb, name='alpha')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-1-f7950d1936ff>:62: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([\"\"]+col_labels)\n",
      "<ipython-input-1-f7950d1936ff>:63: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels([\"\"]+row_labels)\n"
     ]
    },
    {
     "data": {
      "image/png": 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GxqnqjHOeWwn4DngSWAksd+8qDCQDx9yP71fVhAz8b1xT+fLlo02bNoCzOMOkSZMoXrw427Zto3z58lSsmHbRm82bN/Poo49y6tQpfvzxR2rWrMn333/P/fff79c/go8++og77riDs2fPAjBixAi6d+9OuXLl+Oyzz5gzZw6dOnVi7ty5DB06lMOHD7NgwQJ69uzJjBkzaNu2rV/iadu2LV27dqVTp07ebbNnz/b+PnDgQHLnzg3AmDFjmDNnDvv27SMqKorhw4fz5ptvMmDAAL+9Nvny5aNdu3aA835NnDiR4sWLA3Dq1Cn27dtHzpw5veU3btxI48aNOXnyJD/88AMPPfQQ69ato3Llyn6JKTk5mcTERO8XbmxsLMHBwYSHh3vLxMXFec+VmJhIeHg4KSkpJCQkkC1bNuLj4wkNDb3mNcRATqSZFpn7fuefAl+rajFVLQW8BBQCdqnqfap6D9ASeFZE2qd6rgsYDnwOoKp/qGo5VS0HTARGex5fT0n0XL/99hu5c+cmV65cFywTFBREUlISSUlJBAUFceLECc6cOcOtt97qtziOHj3KunXraNiwoXfb/v37KVu2LAAVK1Zk1apVAAQHB5OQkEBcXBzBwcEcPHiQ48ePU65cOb/EUq1aNfLly5fuPlXl448/pkWLFoBTG4uNjSUmJoaQkBB2797NoUOHvMv1+dv+/fvJkyePN5GvXLmSGjVqpElIqd8vl8vlfb9uu+22Cx32iqSkpOByuRARRASXy+WtnYLzGiUlJRESEpJmm6eWmpKSgqpe84Efz2DT5fxkhsyskT4EJLoXCQBAVX8QkX+kLqSqu0WkHzAKmObe3Av4GKcme8PasWMHd999t/fxjz/+yPbt2ylUqBA1atQgW7ZsVKpUia+++org4GDq1avH6tWrqVq1ql/jGDt2LN26dSMmJsa7rWjRoqxdu5bq1avz9ddfc/ToUcBpZr/11luEhYXx8ssvM378eDp27OjXeC7km2++oVChQt4aYf/+/enRowfh4eFMmTKFF198kcGDB2fY+X/55Rfv+7Vz505y5sxJwYIF05SpXLkyX3zxBcHBwTRo0ICvv/6aatWq+S2GoKAg4uPjvWvfehK2R3JycppmdGhoqLeG6qmNhoWF+S2eK409UGVmIi0NbLrMspuBuwFE5BbgcaA2V5lI3Tf1ioSsuwBucnIyu3bt4sEHHwSgTJky3ubft99+y+rVq6lTpw4FCxakVStnjYYDBw6QI0cOAD777DOCgoKoUaMGERERVx3Ht99+S968eSlZsiRbtmzxbn/hhRcYM2YMM2bM4MEHH/TWcEqUKMHEic535w8//ED+/PkBGDx4MMHBwfTo0eOCNUpfzZ8/nyeeeML7uGzZsqxevRqAtWvXUqRIEVSVNm3aEBISwrBhwyhUqJBfzu15v6pXr05iYiLfffddmlg8ChYsyFNPPQU4LY4cOXKgqkRHRxMUFEStWrV8er9cLhehoaHExMR4a6SpnVsbdblc3vMlJSV5a8+xsbEAhIWFXZMEF+h9pIEbWVqpO2PeAQaqavLVHkxVo1S1oqpWjIzMmvf32rt3LwULFvR+yCMiIggKCkJEKF26NIcPH05TXlX5/vvvqVy5Mt999x1VqlThnnvu4YcffvApjq1bt/LNN9/QokULhgwZwubNmxk6dCh33HEHb7/9NpMnT+bhhx/m5ptvPi+emTNn0q5dO6ZNm0aHDh2oU6cOH3/8sU/xXEhSUhILFy6kefPm5+1TVYYNG8aLL77I66+/zqBBg2jVqhXjx4/32/n37Nnjfb9OnDjByZMnmTFjBlFRUZw+fZpZs2Z5+5c9MXnep3Xr1lG1alVKlSrF5s2bfY4lNDSUiIgIsmfPniZBeZr16TWPVZWEhATCwsK8faQhISFpugUymqc74lI/mSEza6TbgPM/1em7D9ju/r0iMNf9guUHGohIkqou8HuEASx1MxHgzJkz3trmrl27uOmmm9KU/+9//0vRokXJli0biYmJ3g+dr38IXbp0oUuXLgBs2bKFuXPnMmjQIP766y/y5s1LSkoKM2fOpHHjxmmet2zZMqpUqULOnDmJj4/3fglk1G1EVqxYwV133ZVu3/Ds2bOpV68eefPmJSYmxrtAcOquCl9t377d+34VKFCAHj3+XqUtKiqKNm3aeAeAALZt28add9553vuVlJTkcywpKSkEBQWRkpJCUlKS97zJyckXXBzZk2A9iUpEvH2n10og10gzM5GuAN4Qkc6qOgm8I/HZUxdy95mOBMYCqGrRVPumA4tvtCSamJjI/v37efjhh73b1qxZw7FjxxARcuXKxf/93/+lKf/f//6Xpk2bAlC+fHmio6O9U6IywldffcWnn34KQI0aNdKcJy4ujmXLljFq1CgAWrRowSuvvEJISIjPfZRPP/00a9as4fjx4xQrVoxBgwbxzDPP8OGHH3oHmVKLiYlh9uzZLF68GIDevXvTqlUrQkNDmTFjxnnlr0ZiYiL79u2jTp06l11+27Zt3tpzxYoVWbRoES6XK82A3tWKi4vzJsCwsLA0I/QXqo16Ru/h70E6IM1of0YK9Ka9XMtvlPNOLnIzTlO9As5d//biTH/6ibTTnyao6rR0nj8dJ5F+lGrba8AZVR15mWFooEz0tZvfXZzd/O7yBVLff7zDp7uIRkREaOoW2MVs3rx5k483v7timTqPVFUPAedXE+CyvuZU9Zl0tr3mW1TGmEAUKBWe9NiVTcaYLCGQm/aWSI0xAS/Q+0gtkRpjsgRLpMYY4yPrIzXGGB/Yws7GGOMH1rQ3xhgfZObln5fDEqkxJkuwGqkxxvjIEqkxxvggvSX/AoklUmNMlmA1UmOM8UGgDzZl6upPAeKGfwGMyUhhYWE+r/6UJ08erVGjxmWVjY6OvrFWfzLGmMtlTfsAN2vWrMwOAXBuJQyBcymcp7WS+l5Mmem+++4DAnM90sutLV0rvt5CJtAEetPeEqkxJuAF+iWigVtXNsaYVPx18zsRqSciO0Rkp4i8kM7+3CISLSI/isg2EWl/qWMGboo3xphU/NFHKiIuYBzwCHAA2CAii1T1v6mK9QD+q6qNRKQAsENE3lfVhAsd1xKpMSbg+XFh5/uBnaq6233cuUBjIHUiVSCnONXbHMCfwEVv32qJ1BiTJfhpsOkW4LdUjw8Alc8p8x6wCDgE5ASeVNWUix3UEqkxJku4ghppfhHZmOpxlKpGuX9PLxufO5e8LvADUBsoBnwpImtU9dSFTmiJ1BgT8K7wWvvjF5mQfwC4LdXjW3Fqnqm1B4apM/9vp4jsAe4G1l/ohDZqb4zJEoKCgi7r5xI2ACVEpKiIhAItcZrxqe0H/g9ARAoBJYHdFzuo1UiNMVmCP/pIVTVJRHoCnwMuYKqqbhORru79E4GhwHQR2YrTFTBQVY9f7LiWSI0xAc+ft2NW1SXAknO2TUz1+yGgzpUc0xKpMSZLCORLRK2P9BLOnj3L2LFjGThwIAMHDuTXX3/lzJkzDB8+nP79+zN8+HDOnj0LwP/+9z9efvllBg8ezJEjR7zPHzFiBNf7KlvJycm0atWK3r17p9k+c+ZMypcvz19//QU414C3aNGCNm3asH//fgBOnz5N9+7d/fIadenShdtvv50KFSp4t7Vp04bKlStTuXJlSpYsSeXKzmyXb7/9lkqVKvHggw+ya9cuAE6cOEGjRo38+n4dPHiQTZs2sWnTJn755RdSUlLYvn07mzdvZvPmzaxfv57NmzcDcPLkSTZt2sSWLVuIjY0FICkpia1bt/otpoSEBM6ePcvZs2dJSHDmmMfGxnq3nTlzxvuZTkpK8m5PSXFmAKkqMTEx1/Qz7RlsupyfzJCpNVIRKQy8A1QC4oG9QF/gR+AXIBtwGhinqjPcz8kNzAZux4l/JE5n8XL3YQsDycAx9+P7L3ZFwqXMnj2be++9l169epGUlER8fDzR0dGUKlWKRo0aER0dzeLFi3nyySdZunQpvXr14vjx4yxfvpzWrVuzcOFCHnvssYD+NvWHOXPmULRoUc6cOePddvjwYb777jsKFy7s3TZr1ixGjhzJoUOH+Oijj+jXrx+TJk2iY8eOfnmN2rZtS9euXenUqZN32+zZs72/Dxw4kNy5cwMwZswY5syZw759+4iKimL48OG8+eabDBgwwG/vV3x8PAcPHqRChQq4XC62b9/OsWPHuOeee7xldu/e7U0ABw8epFSpUsTFxfH7779z5513sn//fm677Ta/xJScnExiYiLZs2cHnAQaHBxMeHi4t0xcXJz3XImJiYSHh5OSkkJCQgLZsmUjPj6e0NDQa/6ZDuTVnzItMvdVA58CX6tqMVUtBbwEFAJ2qep9qnoPzqjas6mud/VcvlUWqAWMAk6rajlVLQdMBEZ7HvuSRGNjY9mxYwc1a9YEIDg4mIiICDZv3kz16tUBqF69Ops2bQLA5XKRkJBAfHw8LpeLI0eO8Ndff3H33XdfbQhZwpEjR1izZg1NmjRJs33UqFH07ds3zR9ccHAwcXFxxMXFERwczG+//cbRo0fT1CB9Ua1aNfLly5fuPlXl448/pkWLFgCEhIQQGxtLTEwMISEh7N69m0OHDnnfW39RVVJSUrz/hoaGptl37NgxChYsCDg1r5SUFFJSUhARYmNjiY+PJ0+ePH6JJSUlBZfL5b0u3eVykZiYmCaepKQkQkJC0mxTVW9sqpopC4j461r7jJCZNdKHgMRzOnl/EJF/pC6kqrtFpB9OwpzGVVy+dbWOHj1Krly5mDRpEvv376do0aK0adOGU6dOeT/YefLk4dQpZ55uo0aNmDZtGqGhoXTp0oU5c+bQrFmzjAgtoIwcOZI+ffoQExPj3bZq1SoKFizIXXfdlaZshw4d+Pe//022bNkYOnQoo0ePpnv37tckzm+++YZChQpRvHhxAPr370+PHj0IDw9nypQpvPjiiwwePNiv5wwLC+PWW29l/fr1BAUFkTdvXvLmzevdf+rUKUJDQ701wttuu41ff/2VoKAgSpYsye7du/nHP/7ht3iCgoKIj4/3NsuTkpLSNIeTk5PTDOyEhoZ6a6ie2mhYWJjf4rlc/hxsygiZmUhLA5sus+xmnAmxcBWXb12t5ORk9u7dS9u2bSlWrBizZ88mOjr6guXvuOMO7x/iL7/8Qp48eVBV3nvvPYKDg2nVqpW3WXm9WL16Nfny5aNUqVJs3OhcTBIbG8uUKVMYN27ceeVLlizJzJkzAdi0aRMFChRAVRk4cCDBwcH069ePm266KUNinT9/Pk888YT3cdmyZVm9ejUAa9eupUiRIqgqbdq0ISQkhGHDhlGoUCGfzpmYmMgff/xBpUqVCA4O5pdffuHo0aPeGujRo0cpUKCAt3yOHDkoV64c4PSXhoWFoaps374dEeHOO+9MU6O9Ui6Xi9DQUGJiYtKd5H5ubdTlchEREeHd56nxefpvw8LCrlmCC+TuscBN8WmlfgU9l2/dDJQD3hORXFd0MJFIEdkoIhujoqIuWC5fvnzky5ePYsWKAVCpUiX27dtHrly5OHHiBOAMTuTKlfb0qsqiRYto0qQJCxYsoGnTplStWpUvvvjiSsLMEn788UdWrVpFw4YNefHFF9m4cSODBg3i4MGDtGzZkoYNG3L06FGeeuopjh//eyqeqjJlyhQ6d+5MVFQUXbt2pUGDBsyZMydD4kxKSmLhwoU0b978vH2qyrBhw3jxxRd5/fXXGTRoEK1atWL8+PE+n/fEiRNky5aN0NBQgoKCuOmmm7wtGFXljz/+SJNIU8fk6Rvdv38/d9xxBwULFuTQoXMvwrlyoaGhREREkD179jQ1PU+zPr1mu6qSkJBAWFiYt480JCQkTbdARvPThPwMkZk10m3A+Z/q9N0HbHf/fsWXb53Lfd2tJ4PqhVbIz5MnD/ny5eP333+nSJEibNu2jZtvvpmbb76ZNWvW0KhRI9asWUP58uXTPG/t2rWULVuWiIgIEhISvH03nhHS60mvXr3o1asXABs3bmTmzJmMHDkyTZmGDRsye/bsNE3a6OhoqlWrRq5cuYiLi/P+EWTU6vcrVqzgrrvu4tZbbz1v3+zZs6lXrx558+YlJibGG0vqroqrFRYWxunTp0lOTiYoKIgTJ06QI0cOAP766y/Cw8PTbSofPXqUfPnyERIS4h0tFxGSk5N9jiklJYWgoCBSUlJISkryDjx5YkwvGXkSrKdWKCLevtNrwW7HfGErgDdEpLOqTgIQkUpA9tSF3H2mI4Gx7k2ey7fWXO7lW75o27YtEyZMIDk5mQIFCtC5c2dSUlIYN24cq1ev5qabbqJnz57e8vHx8axdu5b+/fsDUK9ePcaOHUtwcDDdunXLqDCzlNjYWBYvXuxt+j/11FP079+f4OBg3nzzTZ+O/fTTT7NmzRqOHz9OsWLFGDRoEM888wwffvihd5AptZiYGGbPns3ixYsB6N27N61atSI0NJQZM2b4FAtArly5yJ8/P1u2bEFEyJEjB0WKFAFIM8iUWnJyMkeOHKF06dIA3HLLLWzfvt3bb+qruLg4bwIMCwtLM0J/odqoZ/Qe/h6kA9KM9me0QG7aZ+pdREXkZpzpTxWAOP6e/vQTaac/TVDVaameMx0ogtPkH6aqs1Md8zXgjKqmrRZd2AVrpNea3bPp4uyeTZcvkO7ZFO/w6S6ihQsX1nbt2l1W2REjRtxYdxF1X4p1fjUBLvg1d6nLt1T1Nd8jM8YEErv5nTHG+IFNfzLGGB9ZjdQYY3xgo/bGGOMHViM1xhgfWR+pMcb4wEbtjTHGD6xGaowxPrJEaowxPrBl9Iwxxg+sj9QYY3xkidQYY3xgTfsswLPqUqAItDuOelZdChSeFZcCiWelfXM+f92axGqkxhjjI7tENMD9/PPPmR0CgHchX89dSTOb586egVIT8NTUt2/ffomS147ntsqehbwDxX/+85/MDsGvbEK+Mcb4gSVSY4zxkQ02GWOMj6xGaowxPrA+UmOM8QMbtTfGGB9ZjdQYY3xgTXtjjPEDG7U3xhgfBXKNNHBTvDHGuHkWLbmcn8s4Vj0R2SEiO0XkhQuUqSUiP4jINhFZdaljWo3UGJMl+KNpLyIuYBzwCHAA2CAii1T1v6nK5AHGA/VUdb+IFLxkbD5HdgNITk7miSeeoEePHgCMGjWKRo0a0bRpU/r06cOpU6cA2LJlC02bNqVly5bs378fgFOnTtGlSxe/ruiUnJxM69at6du3L+BcV12/fn1at25N69atWbt2LQA//PADLVu25Omnn+a3334D4PTp0/Ts2TPgVpjyt+TkZJo2bUq3bt0AOHHiBB07dqRevXp07NiRkydPArB582aaNGlCixYt2LdvH+C8Z507d/bLa/Tnn38yY8YM78+7776bZi2FDRs2MHLkSGJiYgA4ePAg06dPZ9asWfz1118AxMXF8dFHH/ntPUtISODs2bOcPXuWhIQEAGJjY73bzpw5w9mzZwFISkrybk9JSQGcNQ9iYmKu+WfIM+B0qZ9LuB/Yqaq7VTUBmAs0PqdMa+ATVd0PoKpHL3XQLJ1IReQZEbk5o88ze/ZsihYt6n1cpUoVPv30Uz755BPuuOMOJk+eDMCMGTMYPXo0vXv3Zt68eYCT5Dp16uTX/p05c+akiQegdevWfPDBB3zwwQdUq1YNgPfff58RI0bQvXt3PvroIwAmT55M+/btA7q/yR9mzZpFsWLFvI8nT57MAw88wLJly3jggQe879n06dN555136Nu3L3PnzgVgwoQJREZG+uU1ypcvH+3ataNdu3a0bduW4OBgihcvDjgJe9++feTMmdNbfuPGjTRu3Jjq1avzww8/ALBu3ToqV67sl3iSk5NJTEwke/bsZM+enaSkJFJSUggPDyciIoKIiAiCg4MJDnYaq4mJiYSHhxMWFuZNuvHx8YSGhl7Tz9DlJlF3TPlFZGOqn8hUh7oF+C3V4wPubandBeQVka9FZJOIPH2p+LJ0IgWeATI0kR4+fJg1a9bQrFkz77aqVat6P2hly5blyJEjAAQHBxMXF0dcXBzBwcH89ttvHD16lEqVKvktniNHjvDNN9/QpEmTS5YNDg4mPj7eG8+BAwc4evSod1Wn69Xhw4dZtWpVmvdsxYoV3tesSZMmLF++HPj7NYqNjSUkJIT9+/f7/T3z2L9/P3ny5CF37twArFy5kho1aqRJSEFBQSQlJZGUlITL5eLEiROcOXOG2267zS8xpKSk4HK5vEnH5XKRmJjo3a+qJCUlERISkmabqiIipKSkoKrez/+1dAWJ9LiqVkz1E5X6MOkc+tyqdTBQAWgI1AUGichdF4vtkolURP4hIr+IyGQR+VlE3heRh0XkGxH5VUTuF5F8IrJARH4Ske9EpIz7ua+JyFR3Zt8tIr1THbef+3g/i0jfVNufdh/nRxGZJSI5RWSPiIS49+cSkb0i8gRQEXjf3SkcLiIVRGSV+1vkcxEpcqn/36WMGDGCZ5999oL9M59++qm3BtipUyf+9a9/MXv2bFq1asW7775Lz549fQ0hjVGjRtG7d+/zagPz58+nZcuWDBkyxNvV8Mwzz/D6668zZ84cWrRowbhx47xN3evZsGHDeP7559O8Z3/88QcFChQAoECBAvz5558AdO7cmcGDBzNr1ixat27NmDFj6NWrV4bE9csvv3D33XcDsHPnTnLmzEnBgmm73ypXrswXX3zBpk2buO+++1izZo338+UPnkTtSY6e3z2Sk5PTrEYfGhpKXFwciYmJhISEEB8f77eFmq+Un5r2B4DU30q3AofSKbNMVc+q6nFgNVD2Yge93K+V4sATQCSwAacPoRrwGPASTlV5i6o2EZHawEygnPu5dwMPATmBHSIyASgDtAcq43xDfO8eGUsAXgYeVNXjIpJPVU+LyNc43w4LgJbAx6r6oYj0AJ5X1Y3uRDsWaKyqx0TkSeB1oMNl/h/Ps2rVKvLly8c///lPNmzYcN7+qKgoXC4Xjz76qPMfvftu3n//fcBpohUoUABV5fnnnyc4OJjnn3+e/PnzX204rFmzhnz58nHPPfewceNG7/bmzZt7uw8mTJjA6NGjGTx4MCVLlmT69OmA0xfoiefFF18kODiYvn37ctNNN111PIHo66+/9r5n69evv2T5e+65x9uk37hxozex9evXj+DgYAYMGODTe+aRnJzMrl27qF69OomJiXz33Xc88cQT55UrWLAgTz31FAC//fYbOXLkQFWJjo4mKCiIWrVqERERcdVxuFwuQkNDiYmJ8dZIUzu3NupyubznS0pK8iaq2NhYwFn9/lrM70wv1qu0ASghIkWBgzj5pPU5ZRYC74lIMBCKk6dGX+ygl/sK7FHVraqaAmwDlqvzNbYV+AdOUp0FoKorgJtEJLf7uZ+parw7sx8FCrnLf+rO+GeAT4DqQG3gI3dZVPVP9zEm4yRe3P9OSyfGkkBp4EsR+QF4Befb5jwiEunpP4mKikqvCOAMHq1cuZK6devSv39/1q9fzwsvOLMlFi5cyKpVqxg2bNh534KqSlRUFF26dGHChAl0796dRx99lA8++OCC57ocP/74I6tXr6ZRo0a8/PLLbNiwgUGDBnHTTTfhcrkICgri8ccfZ9u2befFM2XKFDp16sSkSZPo0qUL9evX9yaQ68nmzZtZuXIlDz/8MM899xzff/89AwYM4KabbuLYsWMAHDt2jHz58qV5nqoyceJEunbtyrhx4+jZsyeNGjVi9uzZfolrz549FCxYkIiICE6cOMHJkyeZMWMGUVFRnD59mlmzZnkHeDzxfPfdd1SpUoV169ZRtWpVSpUqxebNm32OJTQ0lIiICLJnz56m9umpoabXbFdVEhISCAsL8/aRhoSEpOkWyGj+qJGqahLQE/gc2A7MV9VtItJVRLq6y2wHlgE/AeuByap60dXfL7dGGp/q95RUj1Pcx0hKL+Z0npvsLn+h/61wfn8FqvqNu4uhJuC6wH9KgG2qWuWC/4u/jxcFeDKoXmiF/L59+3pHxjds2MD06dMZNmwYa9euZerUqUybNo3w8PDznrdw4UJq1KhB7ty5iYuL885v83yLX62ePXt6uwo2btzI7NmzGTp0KMePH/fWmlauXJlmkAVg8eLFVKtWjVy5chEXF+f944mLi/MpnkDUr18/+vXrB8D69euZNm0aI0aM4K233mLBggV07tyZBQsWULt27TTPW7BgATVr1jzvPfPXa7R9+3Zvs75AgQLeGSDgtGzatGlD9uzZvdu2bdvGnXfeSbZs2UhMTPQmiaSk9P7UrkxKSgpBQUGkpKSQlJTkPW9ycvIF52J6EqwnUYmIt3vgWrmMZvtlUdUlwJJztk085/FbwFuXe0x/9RivBp4ChopILZzO3lMX+Y+vBqaLyDCcBPg40Banaf+piIxW1T/cTXtPrXQmMAcYmuo4p3G6DAB2AAVEpIqqrnM39e9S1bTVMz944403SEhIIDLSGQwsU6YMr776KuA0eRYtWuS91cPTTz/Ns88+S0hICCNGjPB3KACMGTOG//3vf4gIRYoU4eWXX/bui4uLY/HixYwbNw6Ap556igEDBhASEsLrr7+eIfEEos6dO/Pss8/y8ccfU6RIEUaP/rulFhsby8KFC5k0aRIA7dq1o0+fPoSEhDBy5Eifz52YmMi+ffuoU6fOZZfftm0bzZs3B6BixYosWrQIl8tFw4YNfY4nLi7OmwDDwsK8CSoxMfGCtVHP6D1ASEiIt1KQXkUio/grkWYEudQ3ioj8A1isqqXdj6e7H3/k2QfUwGluFwVigEhV/UlEXgPOqOpI93N/Bh5V1b0i0o+/+y8nq+o77jLtgP44tdctqvqMe3thYA9QRFVPuLc1A94AYoEqOM37d4HcOF8S76jqpEu8BheskV5rds+mi7N7Nl2+QLpnU7zDp1u/FitWTC+3ItK8efNNqlrRl/NdqUvWSFV1L07fo+fxMxfYd+6kVlT1tXMepz7O28Db6TxnBjAjnVCq4fSfnkhV9mPg41RlfsBJ6saY60jqvtxAlCUuERWRsUB9oEFmx2KMyRyWSH2kqhkzsc8Yk2UEShdTerJEIjXG3Nguc7J9prFEaozJEiyRGmOMjyyRGmOMj2ywyRhjfGB9pMYY4weWSI0xxkeWSI0xxkeWSI0xxkeWSI0xxgeBfq39JVd/ugHc8C+AMRkpLCzM59WfSpYsqRMnTrx0QaB27dqBt/qTMcb4YsGCBU38cRxr2gc4z21mM1toaCgQePF47pKa2QoVKgQE1h+Up0X3xx9/ZHIkaXnuQX89CaT3/VyWSI0xAc8m5BtjjB8E8mCTJVJjTJZgNVJjjPGRJVJjjPGB9ZEaY4wfWCI1xhgfWSI1xhgf2ai9Mcb4wPpIjTHGDyyRGmOMjwI5kQZup0OAiIyM5NZbb+W+++7zbhs6dChFixalUqVKVKpUiaVLlwLw7bffUqFCBapWrcrOnTsBOHHiBA0bNsRfq2wFWjxxcXHUq1eP2rVrU6NGDUaMGAHAX3/9RYsWLahSpQotWrTgxIkTAKxfv56HHnqIunXrsmfPHgBOnjxJy5Yt/RZTIDl48CCNGzemSpUqPPjgg/znP/8BoGPHjtSqVYtatWpx3333UatWLQC+//57atSowcMPP8zu3bsB5/V54okn/Pb6HDx4kMcff5wHH3yQ6tWrExUVBcDPP/9M/fr1qVmzJm3atOH06dPemGrWrEmdOnXSxNSiRYvr8j27GlkqkYpIsoj8ICLbRORHEeknIkGp9t8vIqtFZIeI/CIik0Ukuy/nbNu2LdHR0edt79WrFxs2bGDDhg3Ur18fgHfeeYe5c+fyr3/9y/vhfOONNxg4cKDfvk0DLZ6wsDA+/vhjVqxYwfLly1m5ciWbNm1i7NixVK9enXXr1lG9enXGjh0LwMSJE5kyZQovvfQS06dPB+Dtt9+md+/eAV3juFoul4t//etfrFu3jmXLljFlyhR27NjBlClT+Prrr/n666959NFHadiwIQDjx49n2rRpvPLKK0ybNg2AkSNH0rdvX7+9PsHBwQwZMoRvvvmGpUuXMnXqVHbs2EG/fv0YNGgQq1atokGDBowbNw6ACRMmMG3atDTv2ahRo/wa0+Xw9JNe6iczZKlECsSqajlV/SfwCNAAGAwgIoWAD4GBqloSuAdYBuT05YTVq1cnb968l1U2JCSEuLg4YmJiCAkJYdeuXRw6dIgaNWr4EkJAxyMiREREAJCYmEhSUhIiwueff06LFi0AaNGiBcuWLQOcP+K4uDhiY2MJCQlh7969HD58mKpVq/otpkBSuHBhypYtC0DOnDm56667+P333737VZWFCxfStGlT4O/Xx/Oe7dmzh99//50HH3zQbzEVKlSIMmXKAJAjRw5vTDt37qRKlSoA1KxZk8WLFwPO5yg2Ntb7nu3Zs+eav2eehZ0v5yczZNk+UlU9KiKRwAYReQ3oAcxQ1XXu/Qp8lFHnnzhxIu+//z4VKlRg+PDh5M2blwEDBtC9e3fCw8OZOnUqL7zwAoMHD86oEAImnuTkZOrUqcOePXto37495cuX59ixY95l7woVKsTx48cB6N27N88//zzZsmXjvffeY8iQIQwcONDvMQWi/fv3s3XrVipUqODdtm7dOgoUKECxYsUA6Nu3L/369SNbtmxMmDCBV199lRdffPGaxHT33XezbNky6tevz6JFizh48CCQ9j0bN24cr732Gi+88EKGxXQhgdxiyWo10jRUdTfO/6EgUBrYdC3OGxkZyfbt29mwYQOFCxf2JoKyZcuyZs0avvjiC/bs2UORIkVQVZ566imeeeaZDFvXM7PjcblcLF++nC1btrBlyxa2b99+wbKlS5dmyZIlfPLJJ+zbt4/ChQujqkRGRtKjRw+OHTvml5gCzZkzZ3jmmWd4/fXXyZnz70bSJ5984q2NAtx77718/vnnLFy4kL1793pfn44dO9K1a1eOHj3q15g6dOjA0KFDyZkzJ2PGjGHq1Kk8/PDDnDlzxrse7b333svSpUv59NNP2bdvH4UKFUJV6dy5M926dfNrTBdjTfuMdcWvnIhEishGEdno6Tu8EoUKFcLlchEUFESHDh3YsGFDmv2qyptvvslLL73E66+/zqBBg2jVqpW3z8nfAiWe3LlzU7VqVVauXEmBAgW8ifrIkSPkz5//vJjeeecdnn32WUaNGkX//v1p1qwZkydP9mtMgSAxMZH27dvTvHlzHn30Ue/2pKQkPvvsMx5//PHznqOqvP322zz//PO89dZbvPDCCzzxxBNMmjTJbzF16NCBZs2aeWMqUaIEH374IV999RVNmzblH//4R7oxPffcc7z11lsMGDCAJ5544pq9Z/5KpCJSzz2OslNELli1FpFK7nGZ5pc6ZpZOpCJyJ5AMHAW2ARUu/gyHqkapakVVrRgZGXnF503dx7Vw4UL++c9/ptk/a9Ys6tevT968eYmJifH23cTExFzxuQI9nuPHj3Py5EkAYmNjWbNmDcWLF6dOnTrMnz8fgPnz51O3bt00z5s3bx4PP/wwefLkITY21htTbGyszzEFElWlT58+3HXXXXTv3j3NvlWrVlG8eHFuvvnm8543d+5cHnnkEe/r4+kj9Md7pqr07duXu+66i27dunm3e1oDKSkpvP3227Rr1y7N8+bNm5cmpqCgIEQkS71nIuICxgH1gVJAKxEpdYFyw4HPL+e4WbaPVEQKABOB91RVReQ9YL2IfKaq37vLtAG+UtXDV3uetm3bsnr1ao4fP86dd97JoEGDWL16NT/++CMiwh133JGmZhcTE8Ps2bP57LPPAOjTpw8tW7YkNDSUmTNn+vJfDsh4jh49Su/evUlOTiYlJYXHHnuMOnXqULFiRSIjI/nggw+45ZZb0tSkYmJimD9/PvPmzQOgS5cudOzYkdDQUCZMmOBzTIHk+++/Z/78+ZQqVco7xenll1/mkUce4dNPP03TrPeIiYlh7ty5fPSR08XfrVs32rdvT0hICFfTgkovpg8//JB77rmHhx56yBvT7t27mTp1KgANGzakVatWaWKaN2+e98uxW7dudOjQgZCQEO+Urozkx7uI3g/sdHcLIiJzgcbAf88p1wv4GKh0WfFlpXlgIpIMbAVCgCRgFvC2qqa491cBRuD0maYAq4FnVfViX+MaaPdICrR47J5NF2b3bLq0jRs31q9fv/4yX45RunRp/fDDDy+rbKlSpS54F1F3M72eqnZyP24LVFbVnqnK3AJ8ANQGpgCLVfWiA9dZqkaqqq5L7F8HVL9G4RhjrqEr+ALNLyIbUz2OUlVPVT69g5xbm3wHZxpl8uWeM0slUmOMuQzHL3Jf+wPAbake3wocOqdMRWCuO4nmBxqISJKqLrjQCS2RGmOyBD916WwASohIUeAg0BJonbqAqhZNdc7pOE37BRc7qCVSY0yW4I9EqqpJItITZzTeBUxV1W0i0tW9f+LVHNcSqTEm4Plzsr2qLgGWnLMt3QSqqs9czjEtkRpjsoRAmq1xLkukxpgswRKpMcb4KJATaZa+RNQYYwKB1UiNMVlCINdILZEaYwKe3UXUGGP8wBKpMcb4yBJpgPOschQoAi0ez6pLgSIQVyy76aabMjuE614gJ1IbtTfGZKgmTZosyOwYMprVSHHuXRMIcuTIAXDN7oFzKQULFgQCbz3SQFpr07PYcKDVllLfF+p64MeFnTNE4EZmjDFZhNVIjTFZQqDV+lOzGqkxxvjIaqTGmCwhkGuklkiNMVmCJVJjjPFRICdS6yM1xhgfWY3UGBPwbNESY4zxg0BOpNa0N8YYH1mN1BiTJQRyjdQSqTEmSwjkRGpN+4s4cOAADRo0oEKFClSqVInx48cD8PLLL1O+fHkeeOABWrVqxYkTJwBYt24dDzzwADVr1mTXrl0AnDhxgiZNmvht6beDBw/y+OOPU61aNWrUqEFUVBQA27Zto0GDBtSsWZM2bdpw+vRpANavX0+tWrWoW7cue/bsAeDkyZM8+eSTfokpLi6OevXqUbt2bWrUqMGIESMA+Ouvv2jRogVVqlShRYsW3tdo/fr1PPTQQ+fF07JlS78vj7djxw7Kly/v/cmTJw9jxozhhRdeoFy5crRr185bdtasWbz77rt+PX+gSkhI4OzZs5w9e5aEhAQAYmNjvdvOnDnD2bNnAUhKSvJu9ywWo6rExMQE5HKGmSXLJVIRUREZlerx8yLymvv310Tkeffv2UTkSxEZfLXnCg4O5o033mDTpk2sWLGCqKgofvnlF2rXrs369ev57rvvKF68OKNGOeGMHTuW2bNnM3jwYCZPngzA8OHDef755/32bRocHMyQIUNYu3YtS5YsYdq0aezYsYN+/frxyiuvsGrVKho0aMC4ceMAmDBhAlOnTuWll15i+vTpALz99tv06dPHLzGFhYXx8ccfs2LFCpYvX87KlSvZtGkTY8eOpXr16qxbt47q1aszduxYACZOnMiUKVPOi6d3795+r3GULFmSzZs3s3nzZjZs2ED27Nlp0qQJ69at44cffiA5OZmtW7cSGxvLzJkz6datm1/PH4iSk5NJTEwke/bsZM+enaSkJFJSUggPDyciIoKIiAiCg4MJDnYaq4mJiYSHhxMWFuZNuvHx8YSGhl7zGqJn5P5SP5khyyVSIB5oKiL5L1RAREKBj4FNqjrkak9UuHBhypUrBzjLkpUsWZJDhw7xf//3f94PWqVKlTh06BAAISEhxMXFERMTQ0hICLt37+b333+nWrVqVxvCeQoVKkSZMmUAZ9m9EiVKcPjwYXbu3EmVKlUAqFmzJp999hngJN7Y2FhiYmIIDg5m7969/P7771StWtUv8YgIERERgPNHl5SUhIjw+eef06JFCwBatGjBsmXLvPHExcURGxtLSEgIe/fu5fDhw36L50KWL19OsWLFyJcvHwkJCaiqN4aRI0fSs2dPQkJCMjSGQJCSkoLL5fImHZfLRWJione/qpKUlJTmtVBVVBURISUlBVX1fv6NIyu+GklAFPAs8HI6+4OBucCvqvqCv066b98+fvrpJypWrJhm+6xZs2jWrBkAzz33HL169SI8PJxJkybx8ssv88orr/grhPPs37+fn3/+mfLly3P33XezbNky6tevT3R0NAcPHgSgT58+PP/882TLlo1x48bx2muv8cILfntZAKeWU6dOHfbs2UP79u0pX748x44d864fWqhQIY4fPw5A7969vfG89957DBkyhIEDB/o1nvTMmzePli1bkjNnTpo2bUqFChWoXbs2uXPnZsOGDQwaNCjDYwgEQUFBxMfHe5vlSUlJuFwu7/7k5OQ0a3+GhoYSFxeHiJAtWzbi4+MJCwu75nEH+jzSrFgjBRgHPCUiudPZNwBIUtW+F3qyiESKyEYR2ejpY7yYM2fO0KZNG4YNG0auXLm829966y2Cg4N58sknAShTpgwrV65kyZIl7Nmzh8KFC6OqtGvXjk6dOvl1weazZ8/SsWNHhg4dSs6cOXnnnXeYNm0ajzzyCGfOnPHerqR06dIsXbqUTz/9lH379nlj6ty5M927d/dLTC6Xi+XLl7Nlyxa2bNnC9u3bL1i2dOnSLFmyhE8++SRNPJGRkfTo0YNjx475HM+5EhISiI6Opnnz5gD079+fzZs3M3LkSF599VWGDBnC5MmTefLJJ3n99df9fv5A4nK5CA0NJSYmhtjY2DRJFDivNupyuYiIiCB79uykpKR4k1lsbCyxsbHXdJFta9r7maqeAmYCvdPZvRaoIiJ3XeT5UapaUVUrRkZGXvRciYmJtGnThhYtWtC4cWPv9vfff5+lS5cyZcqU8948VeWtt95i4MCBDBs2jJdeeoknn3ySCRMmXMH/8uIxdejQgWbNmtGwYUMASpQowfz58/nyyy95/PHHueOOO86LafTo0fTr14+RI0cyYMAAmjdv7u3L9YfcuXNTtWpVVq5cSYECBbwr6x85coT8+dP2xKgq77zzDs8++yyjRo2if//+NGvWzK/xeCxdupT77rvvvHtPbdmyBYC77rqL2bNnM2/ePH7++Wd+/fVXv8cQSEJDQ73JMXXt09OsT6/ZrqokJCQQFhbm7SMNCQlJ0y1wI8uSidTtHaAjEHHO9tVAX2CpiNzsywlUlR49elCyZEl69erl3f7ll18yevRo5s2bR/bs2c973vvvv0/dunXJmzcvMTExBAUFERQURGxsrC/heGN69tlnKVGiBF27dvVu99TkUlJSGD16dJoRaXCatg8//DB58uQhNjbWbzEdP36ckydPAk4tZc2aNRQvXpw6deowf/58AObPn0/dunWvSTzpmTt3Li1btjxvu6c2mpiYSHJyMuA0fWNiYvweQyDx1CJTUlLS1ECTk5O978O5PAnWU2nw/HstR+4DuUaaFftIAVDVP0VkPk4ynXrOvo9FpACwTERqqOqJqznHunXrmDNnDv/85z+9gyGDBw9mwIABxMfHe2uolSpVYsyYMQDExMTwwQcfsHDhQgB69uxJmzZtCA0NZerUqemf6AqsX7+eDz/8kHvuuYfatWsD8NJLL7F7926mTZsGQIMGDWjVqpX3OTExMcybN8+b2Lp27UqHDh0IDQ1l4sSJPsVz9OhRevfuTXJyMikpKTz22GPUqVOHihUrEhkZyQcffMAtt9zCpEmT0sQzf/585s2bB0CXLl3o2LEjoaGhfqu1pz7XV199dd7/c8GCBVSqVImbb3a+ax944AHKli3LvffeS9myZf0aQ6CJi4vzJsCwsDBv8klMTLxgbdQzeg/OoKrnC8+z7UYnWW0umIicUdUc7t8LAXuAEar6mnsa1BlVHene/xpQG6ijqnEXOKTaze/SZze/uzS7+d2lxTuy+XKM++67T1etWnVZZXPnzr1JVSteuqT/ZLkaqSeJun8/AmRP9fi1c8q+BqTZZowx/pblEqkx5sZj05+MMcYP/DXYJCL1RGSHiOwUkfMmVYvIUyLyk/vnWxG5ZKe5JVJjzA1DRFw489DrA6WAViJS6pxie4CaqloGGIpzAdBFWSI1xtxI7gd2qupuVU3AuQqyceoCqvqtqv7lfvgdcOulDmqJ1BiTJfipaX8L8Fuqxwfc2y6kI7D0Uge1wSZjTJZwBYNN+UVkY6rHUarqaZ6nd5B054CKyEM4ifSSqw5ZIjXGXG+OX2Qe6QHgtlSPbwUOnVtIRMoAk4H6qvrHpU5oTXtjzI1kA1BCRIq6l9tsCSxKXUBEbgc+Adqq6v8u56BWIzXGZAn+mEeqqkki0hP4HHABU1V1m4h0de+fCLwK3ASMd58z6VJXSlkiNcbcUFR1CbDknG0TU/3eCeh0Jce0RGqMCXh2ZZMxxlznrEZqjMkSArlGaokUMuUeNBeTN2/ezA4hjdS3VwkEgfgHFWir6pcoUSKzQ0gtsP7AMoA17Y0xxkdWIzXGZAmB2BLxsBqpMcb4yBKpMcb4yJr2xpgswZr2xhhzHbNEaowxPrJEaowxPrI+UmNMlmB9pMYYcx2zRGqMMT6ypr0xJuDZMnpZXOfOnbnlllsoV66cd9vgwYMpX748FStWpEGDBhw65Nzy5dtvv6V8+fJUqVKFnTt3AnDixAkaNmyIarr31/JLPB5vv/02oaGhHD9+/JrF06VLF+644w4qVvx7AfGffvqJWrVqUalSJZo1a8apU6cAWLduHffffz/VqlVj165d3ngee+wxv8VzrqJFi1KmTBnuu+8+KlWqBMDAgQMpW7Ys7dq185abNWsWY8aM8fv54+PjadasGY0aNaJ+/frec/Tp04dGjRrRqFEjatWqRaNGjQDYtGkTjz76KE2bNmXfvn0AnDp1ivbt22fYa2R8d10lUhFJFpEfRORHEdksIlV9PebTTz/N4sWL02x77rnn2Lx5Mxs3bqRBgwa8/vrrAIwePZp58+YxdOhQoqKcmxa+8cYbDBw40G/fpunFA/Dbb7+xfPlybr/9du+2axFP27ZtWbBgQZpt3bt3Z+jQoWzYsIHHHnuM0aNHAzBmzBg++OADhgwZwqRJkwAYNmwY/fv3z9DaxooVK9iyZQsbNmzg5MmTrFu3jh9//JHk5GS2bt1KbGwsM2bMoHv37n4/d2hoKDNnziQ6OppFixaxevVqtmzZwpgxY4iOjiY6Opq6detSp04dAKZMmcJ7771Hv379+OCDDwAYN24cXbt2Dega2Y3uukqkQKyqllPVssCLwJu+HrB69ernLWuXelm5s2fPej/gISEhxMbGEhMTQ3BwMLt27eLgwYPUqFHD1zAuGg/A888/zxtvvJHmj+1axFOtWjXy5cuXZtuvv/5KtWrOHWz/7//+j4ULF54XT0hICLt37+bQoUNUr17db/FcSlBQEAkJCagqsbGxhISE8NZbb9GrVy9CQkL8fj4RISIiAoCkpCSSkpLSvEeqypIlS7w10pCQEOLi4oiLiyM4OJh9+/Zx5MgRKleu7PfYjP9cz32kuYC/MurggwYN4v333ydXrlx8+eWXAAwYMIDu3bsTHh7OtGnTGDhwIK+99lpGheAVHR3NLbfcQtmyZdNsz6x4SpUqxeLFi2nUqBGffPIJBw4cAJxk37NnT8LDw5k8eTIvvfQSr776aobGIiLUrVsXESEyMpLIyEiaNm1K+fLlqV27Nrlz52bjxo0ZGkdycjJNmjRh//79PPXUU2m6ZTZs2ED+/Pn5xz/+AThdJa+88grZsmXjrbfeYvjw4fTt2zfDYstKArlGfr3VSMPdTftfcO5JPTSjTjR06FB2795Nq1atGD9+PADlypVj7dq1fPnll+zZs4ebb74ZVaV169a0a9eOI0eO+D2OmJgYhg0bxuDBg8/blxnxAEycOJGoqCiqVq3K6dOnCQ0NBaBs2bKsWrWKZcuWsXfvXooUKYKq0rZtWzp06JAh8axdu5ZNmzaxZMkSxo8fz+rVqxkwYABbtmxh1KhRDBo0iCFDhjB58mSefPJJ/v3vf/s9BpfLRXR0NGvWrOGnn37if//7+w6/ixcv5tFHH/U+LlWqFB999BGzZ8/mt99+o2DBgqgqffr04bnnnvP2f5vAcr0lUk/T/m6gHjBT0vkaE5FIEdkoIhs9fYdXq2XLlnz66adptqkqb775Ji+99BL//ve/efXVV2ndujXvvfeeT+dKz65du9i7dy8VK1akRIkSHDhwgMqVK3P48OFMiQegZMmSREdH8+2339KiRQuKFi2aZr+qMnz4cF544QXeeOMNXnnlFVq2bOn9QvKnm2++GYCCBQvSpEkT1q9f7923ZcsWAO666y5mzZrFvHnz2LZtW4atdp8rVy4qV67M6tWrAaep/8UXX9CgQYPzyqoq48ePp0ePHowdO5bevXvTuHFjZs6cmSGxGd9cb4nUS1XXAfmBAunsi1LViqpaMTIy8oqPnfoPbfHixZQsWTLN/lmzZlG/fn3y5s1LTEwMQUFBBAUFERsbe8XnupR7772XgwcP8uuvv/Lrr79y66238v3331O4cOFMiQfg6NGjAKSkpDB8+HA6dUp7Z9vZs2dTr169DI/n7NmznD592vv7l19+SenSpb37X331Vf71r3+RmJhIcnIy4PShxsTE+C2GP/74wztrIS4ujm+//ZY777wTwPt7kSJFznveJ598Qq1atcidOzdxcXEEBQUhIhn2nhnfXLd9pCJyN+AC/vDlOG3atGH16tUcP36cokWL8uqrr7J06VL+97//ERQUxO233864ceO85WNiYpg1axZLlji3ze7bty9PPvkkoaGhzJo1y5dQLhhP+/btL1g+o+Np164dq1ev5o8//qB48eK88sornD17lv/85z8ANG7cmKeffjpNPO+//z7R0dEA9O7dm9atWxMSEsKMGTN8jie1I0eO0LRpU8Cp/bVq1Yp69eoBsGDBAipWrOitsT7wwAOUKVOGMmXKnNfX7Itjx44xYMAAUlJSSElJoX79+tSuXRs4v1nvERsby6effsq0adMAaN++PT179iQkJMQ7A8IEFrme5qaJSDKw1fMQeElVP7vE0zQxMTFjA7tMnlHjQIsnUGpB4eHhAAE1n9LTc+SZpxsoAuzmd6iqTyNFFStW1NTdMhfjcrk2qWrFS5f0n+uqRqqqrsyOwRiTMWzU3hhjrmOWSI0xxkeWSI0xxkeWSI0xxkeWSI0xxkeWSI0xxkeWSI0xxkeWSI0xxkeWSI0xxkeWSI0xNxQRqSciO0Rkp4i8kM5+EZF33ft/EpHylzqmJVJjzA1DRFzAOKA+UApoJSKlzilWHyjh/okEJlzquJZIjTE3kvuBnaq6W1UTgLlA43PKNAZmquM7II+InL/WYSqWSI0xWYLnlsyX+rmEW4DfUj0+4N52pWXSuK5Wf7pKkhE3PfNFoMXjWb4uUATiKkDFixfP7BDSCKSlBv1h06ZNn4tI/sssnk1ENqZ6HKWqnlthpPfhOffFupwyaVgiNcYEPFWt56dDHQBuS/X4VuDQVZRJw5r2xpgbyQaghIgUFZFQoCWw6Jwyi4Cn3aP3DwAnVfX3ix3UaqTGmBuGqiaJSE/gc5xbEU1V1W0i0tW9fyKwBGgA7ARigAvfy8fturrViDHGZAZr2htjjI8skRpjjI8skRpjjI8skRpjjI8skRpjjI8skRpjjI8skRpjjI8skRpjjI/+H/2hMnWZs5+gAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im, bar = heatmap(replicated_ratio_df.values, \n",
    "                   list(rb_df.index), \n",
    "                  celltypes,\n",
    "                   cmap='alpha', \n",
    "                   vmin=0, vmax=1)\n",
    "_ = annotate_heatmap(im, \n",
    "                 data=replicated_ratio_df.values, \n",
    "                 valfmt=\"{x:.0%}\", \n",
    "                 textcolors=(\"white\", \"white\"),\n",
    "                 threshold=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-67-21c20a3160c2>:62: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([\"\"]+col_labels)\n",
      "<ipython-input-67-21c20a3160c2>:63: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels([\"\"]+row_labels)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 16})\n",
    "fig, ax = plt.subplots(figsize=(8, 7))\n",
    "im, bar = heatmap(np.flip(rb_df.values, axis=0), \n",
    "                   list(rb_df.index)[::-1], \n",
    "                  celltypes,\n",
    "                   cmap='alpha', \n",
    "                   vmin=0.7, vmax=1)\n",
    "_ = annotate_heatmap(im, \n",
    "                 data=np.flip(rbse_anno_df.values, axis=0), \n",
    "                 valfmt=\"{x:^}\", \n",
    "                 textcolors=(\"white\", \"white\"),\n",
    "                 threshold=1)\n",
    "\n",
    "plt.savefig('rb_values.filtered_results.varyingalpha.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-67-21c20a3160c2>:62: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([\"\"]+col_labels)\n",
      "<ipython-input-67-21c20a3160c2>:63: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels([\"\"]+row_labels)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "discovery_celltype = 'CD4T'\n",
    "fig, axes = plt.subplots(1, 6, figsize=(7, 7), sharey=True)\n",
    "for i, discovery_celltype in enumerate(['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']):\n",
    "    colors = [\"white\", color_dict[discovery_celltype]]\n",
    "    cmap1 = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
    "    im1, bar = heatmap(np.flip(replicated_ratio_df[discovery_celltype].values.reshape((6, 1)),\n",
    "                               axis=0), \n",
    "            list(rb_df.index)[::-1], \n",
    "            [discovery_celltype],\n",
    "                     cmap=cmap1, ax=axes[i], vmin=0, vmax=1)\n",
    "    bar.remove()\n",
    "    _ = annotate_heatmap(im1, \n",
    "                         data=replicated_ratio_df[discovery_celltype].values.reshape((6, 1)), \n",
    "                         valfmt=\"{x:.0%}\", \n",
    "                         textcolors=(\"white\", \"white\"),\n",
    "                         threshold=1)\n",
    "    if i > 0:\n",
    "        axes[i].axis('off')\n",
    "        \n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "plt.savefig('replicated_ratio.filtered_results.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 36x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(0.5, 6))\n",
    "fig.subplots_adjust(bottom=0.5)\n",
    "\n",
    "colors = [\"white\", 'black']\n",
    "cmap = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
    "norm = mpl.colors.Normalize(vmin=0.7, vmax=1)\n",
    "\n",
    "fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),\n",
    "             cax=ax, orientation='vertical')\n",
    "plt.savefig('colorbar.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 36x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(0.5, 6))\n",
    "fig.subplots_adjust(bottom=0.5)\n",
    "\n",
    "colors = [\"white\", 'black']\n",
    "cmap = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
    "norm = mpl.colors.Normalize(vmin=0, vmax=1)\n",
    "\n",
    "fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),\n",
    "             cax=ax, orientation='vertical')\n",
    "plt.savefig('colorbar.replication_ratio.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### celltype comparison for unfiltered results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filtered results\n",
    "unrb_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
    "                     columns=celltypes, index=celltypes)\n",
    "unrbse_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
    "                       columns=celltypes, index=celltypes)\n",
    "unrbpvalue_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
    "                           columns=celltypes, index=celltypes)\n",
    "unnumcoeqtl_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
    "                           columns=celltypes, index=celltypes)\n",
    "unanno_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
    "                           columns=celltypes, index=celltypes)\n",
    "unnum_anno_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
    "                           columns=celltypes, index=celltypes)\n",
    "\n",
    "for discovery_celltype in celltypes:\n",
    "    for replication_celltype in celltypes:\n",
    "        if discovery_celltype != replication_celltype:\n",
    "            unrb_results = unfiltered_res_df[(unfiltered_res_df['celltype_discovery'] == discovery_celltype) &\n",
    "                                           (unfiltered_res_df['celltype_replication'] == replication_celltype)]\n",
    "            unreplicated_coeqtls_num = pd.read_csv(\n",
    "                workdir/f'output/unfiltered_results/rb_calculations/discovery_{discovery_celltype}_replication_{replication_celltype}.tsv.gz',\n",
    "                compression='gzip',\n",
    "                sep='\\t',\n",
    "                index_col=0\n",
    "            ).shape[0]\n",
    "            if rb_results['r'].values[0] < 10 and discovery_celltype != 'B':\n",
    "                unrb_df.loc[replication_celltype, discovery_celltype] = unrb_results['r'].values[0]\n",
    "                unrbse_df.loc[replication_celltype, discovery_celltype] = unrb_results['se_r'].values[0]\n",
    "                unrbpvalue_df.loc[replication_celltype, discovery_celltype] = unrb_results['p'].values[0]\n",
    "                unnumcoeqtl_df.loc[replication_celltype, discovery_celltype] = unreplicated_coeqtls_num\n",
    "                unrbvalue = unrb_results['r'].values[0]\n",
    "                unrbsevalue = unrb_results['se_r'].values[0]\n",
    "                unnum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"N={unreplicated_coeqtls_num}\"\n",
    "                unanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"{unrbvalue:.2f}\\nN={unreplicated_coeqtls_num}\"\n",
    "            elif discovery_celltype == 'B':\n",
    "                unrb_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
    "                unrbse_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
    "                unrbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
    "                unnumcoeqtl_df.loc[replication_celltype, discovery_celltype] = unreplicated_coeqtls_num\n",
    "                unrbvalue = unrb_results['r'].values[0]\n",
    "                unrbsevalue = unrb_results['se_r'].values[0]\n",
    "                unnum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"N={unreplicated_coeqtls_num}\"\n",
    "                unanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"N={unreplicated_coeqtls_num}\"\n",
    "            else:\n",
    "                unrb_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
    "                unrbse_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
    "                unrbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
    "                unnumcoeqtl_df.loc[replication_celltype, discovery_celltype] = unreplicated_coeqtls_num\n",
    "                unnum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"N={unreplicated_coeqtls_num}\"\n",
    "                unanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"N={unreplicated_coeqtls_num}\"\n",
    "        else:\n",
    "            unrb_df.loc[replication_celltype, discovery_celltype] = 1\n",
    "            unrbse_df.loc[replication_celltype, discovery_celltype] = 0\n",
    "            unrbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
    "            unreplicated_coeqtls_num = pd.read_csv(\n",
    "                workdir/f'output/unfiltered_results/UT_{discovery_celltype}/coeqtls_fullresults_fixed.sig.tsv.gz',\n",
    "                compression='gzip',\n",
    "                sep='\\t'\n",
    "            ).shape[0]\n",
    "            unnumcoeqtl_df.loc[replication_celltype, discovery_celltype] = unreplicated_coeqtls_num\n",
    "            unnum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"N={unreplicated_coeqtls_num}\"\n",
    "            unanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"N={unreplicated_coeqtls_num}\"\n",
    "            \n",
    "unreplicated_ratio_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
    "                                   columns=celltypes, index=celltypes)\n",
    "for discovery_celltype in unnumcoeqtl_df.columns:\n",
    "    for replication_celltype in unnumcoeqtl_df.index:\n",
    "        unreplicated_ratio_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "        unnumcoeqtl_df.loc[replication_celltype, discovery_celltype] / unnumcoeqtl_df.loc[discovery_celltype, \n",
    "                                                                                          discovery_celltype]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-67-21c20a3160c2>:62: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([\"\"]+col_labels)\n",
      "<ipython-input-67-21c20a3160c2>:63: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels([\"\"]+row_labels)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 14})\n",
    "fig, axes = plt.subplots(1, 6, figsize=(7, 7), sharey=True)\n",
    "for i, discovery_celltype in enumerate(['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']):\n",
    "    colors = [\"white\", color_dict[discovery_celltype]]\n",
    "    cmap1 = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
    "    im1, bar = heatmap(np.flip(unreplicated_ratio_df[discovery_celltype].values.reshape((6, 1)), \n",
    "                               axis=0), \n",
    "            list(rb_df.index)[::-1], \n",
    "            [discovery_celltype],\n",
    "                     cmap=cmap1, ax=axes[i], vmin=0, vmax=1)\n",
    "    bar.remove()\n",
    "    _ = annotate_heatmap(im1, \n",
    "                         data=unreplicated_ratio_df[discovery_celltype].values.reshape((6, 1)), \n",
    "                         valfmt=\"{x:.0%}\", \n",
    "                         textcolors=(\"white\", \"white\"),\n",
    "                         threshold=1)\n",
    "    if i > 0:\n",
    "        axes[i].axis('off')\n",
    "        \n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "plt.savefig('replication_ratio.unfiltered_results.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-67-21c20a3160c2>:62: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([\"\"]+col_labels)\n",
      "<ipython-input-67-21c20a3160c2>:63: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels([\"\"]+row_labels)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 14})\n",
    "discovery_celltype = 'CD4T'\n",
    "fig, axes = plt.subplots(1, 6, figsize=(7, 7), sharey=True)\n",
    "for i, discovery_celltype in enumerate(['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']):\n",
    "    colors = [\"white\", color_dict[discovery_celltype]]\n",
    "    cmap1 = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
    "    im1, bar = heatmap(np.flip(unrb_df[discovery_celltype].values.reshape((6, 1)), \n",
    "                               axis=0),\n",
    "            list(rb_df.index)[::-1], \n",
    "            [discovery_celltype],\n",
    "                     cmap=cmap1, ax=axes[i], vmin=0, vmax=1)\n",
    "    bar.remove()\n",
    "    _ = annotate_heatmap(im1, \n",
    "                         data=unanno_df[discovery_celltype].values.reshape((6, 1)), \n",
    "                         valfmt=\"{x:^}\", \n",
    "                         textcolors=(\"white\", \"white\"),\n",
    "                         threshold=1)\n",
    "    if i > 0:\n",
    "        axes[i].axis('off')\n",
    "        \n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "plt.savefig('rb_values.unfiltered_results.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BIOS replication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "bios_replication_filtered_df = pd.read_csv(\n",
    "    workdir/'bios/onlyRNAAlignMetrics_rmLLD/filtered_results/replication_summary.csv', \n",
    "    index_col=0\n",
    ").set_index('celltype')\n",
    "bios_replication_unfiltered_df = pd.read_csv(\n",
    "    workdir/'bios/onlyRNAAlignMetrics_rmLLD/unfiltered_results/replication_summary.csv', \n",
    "    index_col=0\n",
    ").set_index('celltype')\n",
    "color_dict = {'CD4T': '#2E9D33',\n",
    "              'CD8T': 'darkgreen',\n",
    "              'monocyte': '#EDBA1B',\n",
    "              'NK': '#E64B50',\n",
    "              'DC': '#965EC8',\n",
    "              'B': '#009DDB',\n",
    "              'cMono': 'peru',\n",
    "              'ncMono': 'y',\n",
    "              'CD4T_individual_100': '#2E9D33',\n",
    "              'CD4T_individual_50': '#2E9D33',\n",
    "              'CD4T_50': '#2E9D33',\n",
    "              'CD4T_150': '#2E9D33',\n",
    "              'CD4T_250': '#2E9D33'}\n",
    "\n",
    "bios_replication_filtered_df['color'] = [color_dict.get(celltype) for celltype in \n",
    "                                         bios_replication_filtered_df.index]\n",
    "bios_replication_unfiltered_df['color'] = [color_dict.get(celltype) for celltype in \n",
    "                                          bios_replication_unfiltered_df.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "bios_replication_filtered_df_clean = bios_replication_filtered_df.drop(index=['B'])\n",
    "bios_replication_filtered_df_clean = bios_replication_filtered_df_clean.drop(columns=['color'])\n",
    "bios_replication_filtered_df_clean.to_excel(workdir/'output/summary/rb_values_bios_replication_filtered_results.xlsx')\n",
    "\n",
    "bios_replication_unfiltered_df_clean = bios_replication_unfiltered_df.drop(index=['B'])\n",
    "bios_replication_unfiltered_df_clean = bios_replication_unfiltered_df_clean.drop(columns=['color'])\n",
    "bios_replication_unfiltered_df_clean.to_excel(workdir/'output/summary/rb_values_bios_replication_unfiltered_results.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-12-08dd41d53262>:3: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \".\" (-> marker='.'). The keyword argument will take precedence.\n",
      "  ax2.errorbar(y=bios_replication_filtered_df.loc[sorted_celltypes]['r'].values,\n",
      "<ipython-input-12-08dd41d53262>:8: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \".\" (-> marker='.'). The keyword argument will take precedence.\n",
      "  ax2.errorbar(y=bios_replication_unfiltered_df.loc[sorted_celltypes]['r'].values,\n",
      "<ipython-input-12-08dd41d53262>:12: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax2.set_xticklabels(['', 'CD4T', '', 'CD8T', '', 'monocyte', '', 'DC', '', 'NK'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'NK']\n",
    "fig, ax2 = plt.subplots()\n",
    "ax2.errorbar(y=bios_replication_filtered_df.loc[sorted_celltypes]['r'].values,\n",
    "             x=[ind for ind in range(len(sorted_celltypes))],\n",
    "             yerr=bios_replication_filtered_df.loc[sorted_celltypes]['se_r'].values,\n",
    "             fmt='.', markersize=6, marker='o', color='black', label = 'Filtered results')\n",
    "bios_replication_unfiltered_df.loc['DC'] = [np.nan, np.nan, np.nan, np.nan]\n",
    "ax2.errorbar(y=bios_replication_unfiltered_df.loc[sorted_celltypes]['r'].values,\n",
    "             x=[ind+0.05 for ind in range(len(sorted_celltypes))],\n",
    "             yerr=bios_replication_unfiltered_df.loc[sorted_celltypes]['se_r'].values,\n",
    "             fmt='.', markersize=6, marker='o', markerfacecolor='white', color='black', label = 'Unfilter results')\n",
    "ax2.set_xticklabels(['', 'CD4T', '', 'CD8T', '', 'monocyte', '', 'DC', '', 'NK'])\n",
    "plt.legend()\n",
    "plt.ylabel(\"rb (SE)\")\n",
    "plt.savefig('sf20.comparison_rb_values_bios_replication.pdf')\n",
    "plt.savefig('sf20.comparison_rb_values_bios_replication.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-31-c42524d09425>:14: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \".\" (-> marker='.'). The keyword argument will take precedence.\n",
      "  ax.errorbar(y=bios_replication_filtered_df.loc[celltype]['r'],\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare between filtered and unfiltered\n",
    "fig, axes = plt.subplots(1, 5, figsize=(4, 5), sharey=True)\n",
    "sorted_celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'NK']\n",
    "# ax1.errorbar(y=bios_replication_filtered_df.loc[sorted_celltypes]['r'].values,\n",
    "#              x=[ind-0.1 for ind in range(len(sorted_celltypes))],\n",
    "#              yerr=bios_replication_filtered_df.loc[sorted_celltypes]['se_r'].values,\n",
    "#              fmt='.', markersize=6, marker='o', \n",
    "#              ecolor=bios_replication_filtered_df.loc[sorted_celltypes]['color'].values,\n",
    "#              color=bios_replication_filtered_df.loc[sorted_celltypes]['color'].values[0])\n",
    "# ax1.set_xticklabels([\"\"]+sorted_celltypes)\n",
    "# ax1.plot([0, 5], [0.5, 0.5], linestyle='--', color='black')\n",
    "for ind, celltype in enumerate(sorted_celltypes):\n",
    "    ax = axes[ind]\n",
    "    ax.errorbar(y=bios_replication_filtered_df.loc[celltype]['r'],\n",
    "             x=[0.4],\n",
    "             yerr=bios_replication_filtered_df.loc[celltype]['se_r'],\n",
    "             fmt='.', markersize=6, marker='o', ecolor='black',\n",
    "             markeredgecolor='black', markerfacecolor='black'\n",
    "             )\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.spines['bottom'].set_color(bios_replication_filtered_df.loc[celltype]['color'])\n",
    "    ax.spines['top'].set_color(bios_replication_filtered_df.loc[celltype]['color']) \n",
    "    ax.spines['right'].set_color(bios_replication_filtered_df.loc[celltype]['color'])\n",
    "    ax.spines['left'].set_color(bios_replication_filtered_df.loc[celltype]['color'])\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_xlabel(celltype)\n",
    "    \n",
    "\n",
    "plt.savefig('bios_replication.filtered_results.pdf')\n",
    "plt.savefig('bios_replication.filtered_results.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-32-cb6cab60ed6f>:14: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \".\" (-> marker='.'). The keyword argument will take precedence.\n",
      "  ax.errorbar(y=bios_replication_filtered_df.loc[celltype]['r'],\n",
      "<ipython-input-32-cb6cab60ed6f>:20: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \".\" (-> marker='.'). The keyword argument will take precedence.\n",
      "  ax.errorbar(y=bios_replication_unfiltered_df.loc[celltype]['r'],\n",
      "/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/Beeline/miniconda/envs/scpy3.8/lib/python3.8/site-packages/numpy/core/_asarray.py:102: UserWarning: Warning: converting a masked element to nan.\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare between filtered and unfiltered\n",
    "fig, axes = plt.subplots(1, 6, figsize=(12, 6), sharey=True)\n",
    "sorted_celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']\n",
    "# ax1.errorbar(y=bios_replication_filtered_df.loc[sorted_celltypes]['r'].values,\n",
    "#              x=[ind-0.1 for ind in range(len(sorted_celltypes))],\n",
    "#              yerr=bios_replication_filtered_df.loc[sorted_celltypes]['se_r'].values,\n",
    "#              fmt='.', markersize=6, marker='o', \n",
    "#              ecolor=bios_replication_filtered_df.loc[sorted_celltypes]['color'].values,\n",
    "#              color=bios_replication_filtered_df.loc[sorted_celltypes]['color'].values[0])\n",
    "# ax1.set_xticklabels([\"\"]+sorted_celltypes)\n",
    "# ax1.plot([0, 5], [0.5, 0.5], linestyle='--', color='black')\n",
    "for ind, celltype in enumerate(sorted_celltypes):\n",
    "    ax = axes[ind]\n",
    "    ax.errorbar(y=bios_replication_filtered_df.loc[celltype]['r'],\n",
    "             x=[0.4],\n",
    "             yerr=bios_replication_filtered_df.loc[celltype]['se_r'],\n",
    "             fmt='.', markersize=6, marker='o', ecolor='black',\n",
    "             markeredgecolor='black', markerfacecolor='black'\n",
    "             )\n",
    "    ax.errorbar(y=bios_replication_unfiltered_df.loc[celltype]['r'],\n",
    "             x=[0.6],\n",
    "             yerr=bios_replication_unfiltered_df.loc[celltype]['se_r'],\n",
    "             fmt='.', markersize=6, marker='o', ecolor='black',\n",
    "             markeredgecolor='black', markerfacecolor='white')\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.spines['bottom'].set_color(bios_replication_filtered_df.loc[celltype]['color'])\n",
    "    ax.spines['top'].set_color(bios_replication_filtered_df.loc[celltype]['color']) \n",
    "    ax.spines['right'].set_color(bios_replication_filtered_df.loc[celltype]['color'])\n",
    "    ax.spines['left'].set_color(bios_replication_filtered_df.loc[celltype]['color'])\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_xlabel(celltype)\n",
    "    \n",
    "\n",
    "plt.savefig('bios_replication_comparison.filter_and_unfilter.pdf')\n",
    "plt.savefig('bios_replication_comparison.filter_and_unfilter.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare between filtered and unfiltered\n",
    "celltypes = ['CD4T', 'CD8T', 'monocyte', 'B', 'NK', 'DC']\n",
    "fig, axes = plt.subplots(6, 2, figsize=(12, 12), sharex=True)\n",
    "for i, celltype in enumerate(celltypes):\n",
    "    replication_celltypes = [ct for ct in celltypes]\n",
    "    ax1, ax2 = axes[i, :]\n",
    "    ax1.scatter(x=replication_celltypes,\n",
    "                 y=numcoeqtl_df[celltype].loc[replication_celltypes])\n",
    "    ax1.scatter(x=replication_celltypes,\n",
    "                y=unnumcoeqtl_df[celltype].loc[replication_celltypes])\n",
    "    ax2.errorbar(x=replication_celltypes, fmt='.', markersize=12,\n",
    "                 y=rb_df[celltype].loc[replication_celltypes],\n",
    "                 yerr=rbse_df[celltype].loc[replication_celltypes], label='filtered')\n",
    "    ax2.errorbar(x=replication_celltypes, fmt='.', markersize=12,\n",
    "                 y=unrb_df[celltype].loc[replication_celltypes],\n",
    "                 yerr=unrbse_df[celltype].loc[replication_celltypes], label='Unfiltered')\n",
    "    ax1.set_ylabel(celltype)\n",
    "ax2.legend()\n",
    "\n",
    "plt.savefig('celltype_rb.comparison_filtered_unfiltered_results.pdf')\n",
    "plt.savefig('celltype_rb.comparison_filtered_unfiltered_results.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sub celltypes in monocytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r</th>\n",
       "      <th>se_r</th>\n",
       "      <th>p</th>\n",
       "      <th>celltype_discovery</th>\n",
       "      <th>celltype_replication</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.971431</td>\n",
       "      <td>0.048402</td>\n",
       "      <td>1.351820e-89</td>\n",
       "      <td>ncMono</td>\n",
       "      <td>cMono</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.929081</td>\n",
       "      <td>0.088678</td>\n",
       "      <td>1.101982e-25</td>\n",
       "      <td>ncMono</td>\n",
       "      <td>monocyte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.936797</td>\n",
       "      <td>0.025409</td>\n",
       "      <td>1.468276e-297</td>\n",
       "      <td>cMono</td>\n",
       "      <td>ncMono</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.999726</td>\n",
       "      <td>0.000613</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>cMono</td>\n",
       "      <td>monocyte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.896203</td>\n",
       "      <td>0.036240</td>\n",
       "      <td>5.115902e-135</td>\n",
       "      <td>monocyte</td>\n",
       "      <td>ncMono</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.949824</td>\n",
       "      <td>0.008640</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>monocyte</td>\n",
       "      <td>cMono</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          r      se_r              p celltype_discovery celltype_replication\n",
       "1  0.971431  0.048402   1.351820e-89             ncMono                cMono\n",
       "2  0.929081  0.088678   1.101982e-25             ncMono             monocyte\n",
       "3  0.936797  0.025409  1.468276e-297              cMono               ncMono\n",
       "4  0.999726  0.000613   0.000000e+00              cMono             monocyte\n",
       "5  0.896203  0.036240  5.115902e-135           monocyte               ncMono\n",
       "6  0.949824  0.008640   0.000000e+00           monocyte                cMono"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_mono_res_df = pd.read_csv(workdir/'output/filtered_results/rb_calculations/monocyte_subcelltypes/summary.csv', \n",
    "                                   index_col=0)\n",
    "filtered_mono_res_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filtered results\n",
    "mono_subcelltypes = ['monocyte', 'cMono', 'ncMono']\n",
    "monorb_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
    "                     columns=mono_subcelltypes, index=mono_subcelltypes)\n",
    "monorbse_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
    "                       columns=mono_subcelltypes, index=mono_subcelltypes)\n",
    "monorbpvalue_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
    "                       columns=mono_subcelltypes, index=mono_subcelltypes)\n",
    "mononumcoeqtl_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
    "                       columns=mono_subcelltypes, index=mono_subcelltypes)\n",
    "monoanno_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
    "                       columns=mono_subcelltypes, index=mono_subcelltypes)\n",
    "mononum_anno_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
    "                     columns=mono_subcelltypes, index=mono_subcelltypes)\n",
    "\n",
    "for discovery_celltype in mono_subcelltypes:\n",
    "    # replication in other celltypes\n",
    "    for replication_celltype in mono_subcelltypes:\n",
    "        if discovery_celltype != replication_celltype:\n",
    "            monorb_results = filtered_mono_res_df[(filtered_mono_res_df['celltype_discovery'] == discovery_celltype) &\n",
    "                                         (filtered_mono_res_df['celltype_replication'] == replication_celltype)]\n",
    "            monoreplicated_coeqtls_num = pd.read_csv(\n",
    "                workdir/f'output/filtered_results/rb_calculations/monocyte_subcelltypes/discovery_{discovery_celltype}_replication_{replication_celltype}.tsv.gz',\n",
    "                compression='gzip',\n",
    "                sep='\\t',\n",
    "                index_col=0\n",
    "            ).shape[0]\n",
    "            if monorb_results['r'].values[0] < 10:\n",
    "                monorb_df.loc[replication_celltype, discovery_celltype] = monorb_results['r'].values[0]\n",
    "                monorbse_df.loc[replication_celltype, discovery_celltype] = monorb_results['se_r'].values[0]\n",
    "                monorbpvalue_df.loc[replication_celltype, discovery_celltype] = monorb_results['p'].values[0]\n",
    "                mononumcoeqtl_df.loc[replication_celltype, discovery_celltype] = monoreplicated_coeqtls_num\n",
    "                monorbvalue = monorb_results['r'].values[0]\n",
    "                monorbsevalue = monorb_results['se_r'].values[0]\n",
    "                monoanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"rb={monorbvalue:.2f}\\nN={monoreplicated_coeqtls_num}\"\n",
    "                mononum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"N={monoreplicated_coeqtls_num}\"\n",
    "            else:\n",
    "                monorb_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
    "                monorbse_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
    "                monorbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
    "                mononumcoeqtl_df.loc[replication_celltype, discovery_celltype] = monoreplicated_coeqtls_num\n",
    "                monoanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"rb=NA\\nN={monoreplicated_coeqtls_num}\"\n",
    "                mononum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"N={monoreplicated_coeqtls_num}\"\n",
    "        else:\n",
    "            monorb_df.loc[replication_celltype, discovery_celltype] = 1\n",
    "            monorbse_df.loc[replication_celltype, discovery_celltype] = 0\n",
    "            monorbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
    "            monoreplicated_coeqtls_num = pd.read_csv(\n",
    "                workdir/f'output/filtered_results/UT_{discovery_celltype}/coeqtls_fullresults_fixed.sig.tsv.gz',\n",
    "                compression='gzip',\n",
    "                sep='\\t'\n",
    "            ).shape[0]\n",
    "            mononumcoeqtl_df.loc[replication_celltype, discovery_celltype] = monoreplicated_coeqtls_num\n",
    "            monoanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"N={monoreplicated_coeqtls_num}\"\n",
    "            mononum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "                f\"N={monoreplicated_coeqtls_num}\"\n",
    "    \n",
    "monoreplicated_ratio_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
    "                       columns=mono_subcelltypes, index=mono_subcelltypes)\n",
    "for discovery_celltype in mononumcoeqtl_df.columns:\n",
    "    for replication_celltype in mononumcoeqtl_df.index:\n",
    "        monoreplicated_ratio_df.loc[replication_celltype, discovery_celltype] = \\\n",
    "        mononumcoeqtl_df.loc[replication_celltype, discovery_celltype] / mononumcoeqtl_df.loc[discovery_celltype, discovery_celltype]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>monocyte</th>\n",
       "      <th>cMono</th>\n",
       "      <th>ncMono</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>monocyte</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.826087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cMono</th>\n",
       "      <td>0.996441</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.826087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ncMono</th>\n",
       "      <td>0.985765</td>\n",
       "      <td>0.980645</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          monocyte     cMono    ncMono\n",
       "monocyte  1.000000  1.000000  0.826087\n",
       "cMono     0.996441  1.000000  0.826087\n",
       "ncMono    0.985765  0.980645  1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monoreplicated_ratio_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-1-1162ad6d26ab>:60: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([\"\"]+col_labels)\n",
      "<ipython-input-1-1162ad6d26ab>:61: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels([\"\"]+row_labels)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
    "ax1, ax2 = axes\n",
    "\n",
    "im1, bar = heatmap(monoreplicated_ratio_df.values, \n",
    "        list(monorb_df.index), \n",
    "        list(monorb_df.columns),\n",
    "                 cmap=\"viridis\",\n",
    "                 ax=ax1)\n",
    "\n",
    "\n",
    "_ = annotate_heatmap(im1, \n",
    "                     data=monoreplicated_ratio_df.values, \n",
    "                     valfmt=\"{x:.0%}\", \n",
    "                     color=\"black\",\n",
    "                     threshold=1)\n",
    "\n",
    "im2, bar = heatmap(monorb_df.values, \n",
    "        list(monorb_df.index), \n",
    "        list(monorb_df.columns),\n",
    "                 cmap=\"viridis\",\n",
    "                 ax=ax2)\n",
    "\n",
    "\n",
    "_ = annotate_heatmap(im2, \n",
    "                     data=monoanno_df.values, \n",
    "                     valfmt=\"{x:^}\", \n",
    "                     color=\"black\",\n",
    "                     threshold=1)\n",
    "\n",
    "plt.savefig('cmono_ncmono_mono.filtered_results.pdf')\n",
    "plt.savefig('cmono_ncmono_mono.filtered_results.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Non-zero ratio and co-expression mean and variances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "celltype = 'monocyte'\n",
    "annotated_coeqtl_df = pd.DataFrame()\n",
    "for celltype in celltypes:\n",
    "    celltype_annotated_coeqtl_df = pd.read_csv(\n",
    "        workdir/f'output/filtered_results/UT_{celltype}/coeqtls_fullresults_fixed.all.annotated.tsv.gz',\n",
    "                                     compression='gzip',\n",
    "                                     sep='\\t'\n",
    "    )[['mean_onemillionv2', 'var_onemillionv2', \n",
    "       'gene2_nonzeroratio_onemillionv2',\n",
    "       'eqtlgene_nonzeroratio_onemillionv2',\n",
    "       'gene2_isSig']]\n",
    "    celltype_annotated_coeqtl_df['celltype'] = celltype\n",
    "    annotated_coeqtl_df = pd.concat([annotated_coeqtl_df, \n",
    "                                     celltype_annotated_coeqtl_df],\n",
    "                                   axis=0)\n",
    "    \n",
    "annotated_coeqtl_df_clean = annotated_coeqtl_df.replace([np.inf, -np.inf], np.nan, inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='celltype', ylabel='mean_onemillionv2'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'],\n",
    "            y=abs(annotated_coeqtl_df_clean['mean_onemillionv2']),\n",
    "            hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
    "            fliersize=1,\n",
    "              palette='viridis',\n",
    "              showfliers = False)\n",
    "# plt.savefig('mean_onemillionv2.filtered_results.pdf')\n",
    "# plt.savefig('mean_onemillionv2.filtered_results.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='celltype', ylabel='var_onemillionv2'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'], \n",
    "            y=annotated_coeqtl_df_clean['var_onemillionv2'],\n",
    "              hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
    "              palette='viridis', fliersize=1,\n",
    "              showfliers = False)\n",
    "# plt.savefig('var_onemillionv2.filtered_results.pdf')\n",
    "# plt.savefig('var_onemillionv2.filtered_results.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='celltype', ylabel='gene2_nonzeroratio_onemillionv2'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'],\n",
    "            y=annotated_coeqtl_df_clean['gene2_nonzeroratio_onemillionv2'],\n",
    "            hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
    "            palette='viridis', fliersize=1, showfliers = False)\n",
    "# plt.savefig('gene2_nonzeroratio_onemillionv2.filtered_results.pdf')\n",
    "# plt.savefig('gene2_nonzeroratio_onemillionv2.filtered_results.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='celltype', ylabel='eqtlgene_nonzeroratio_onemillionv2'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'], \n",
    "            y=annotated_coeqtl_df_clean['eqtlgene_nonzeroratio_onemillionv2'],\n",
    "            hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
    "           palette='viridis', fliersize=1, showfliers = False)\n",
    "# plt.savefig('eqtlgene_nonzeroratio_onemillionv2.filtered_results.pdf')\n",
    "# plt.savefig('eqtlgene_nonzeroratio_onemillionv2.filtered_results.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### unfiltered results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CD4T\n",
      "CD8T\n",
      "monocyte\n",
      "DC\n",
      "NK\n",
      "B\n"
     ]
    }
   ],
   "source": [
    "celltype = 'monocyte'\n",
    "annotated_coeqtl_df = pd.DataFrame()\n",
    "for celltype in celltypes:\n",
    "    print(celltype)\n",
    "    celltype_annotated_coeqtl_df = pd.read_csv(workdir/f'output/unfiltered_results/UT_{celltype}/coeqtls_fullresults_fixed.all.annotated.tsv.gz',\n",
    "                                     compression='gzip',\n",
    "                                     sep='\\t')[['mean_onemillionv2', 'var_onemillionv2', \n",
    "                                                'gene2_nonzeroratio_onemillionv2',\n",
    "                                                'eqtlgene_nonzeroratio_onemillionv2',\n",
    "                                                'gene2_isSig']]\n",
    "    celltype_annotated_coeqtl_df['celltype'] = celltype\n",
    "    annotated_coeqtl_df = pd.concat([annotated_coeqtl_df, \n",
    "                                     celltype_annotated_coeqtl_df],\n",
    "                                   axis=0)\n",
    "    \n",
    "annotated_coeqtl_df_clean = annotated_coeqtl_df.replace([np.inf, -np.inf], np.nan, inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'],\n",
    "            y=abs(annotated_coeqtl_df_clean['mean_onemillionv2']),\n",
    "            hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
    "            fliersize=1,\n",
    "              palette='Paired',\n",
    "              showfliers = False)\n",
    "plt.savefig('mean_onemillionv2.unfiltered_results.pdf')\n",
    "plt.savefig('mean_onemillionv2.unfiltered_results.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'], \n",
    "            y=annotated_coeqtl_df_clean['var_onemillionv2'],\n",
    "              hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
    "              palette='Paired', fliersize=1,\n",
    "              showfliers = False)\n",
    "plt.savefig('var_onemillionv2.unfiltered_results.pdf')\n",
    "plt.savefig('var_onemillionv2.unfiltered_results.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'],\n",
    "            y=annotated_coeqtl_df_clean['gene2_nonzeroratio_onemillionv2'],\n",
    "            hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
    "            palette='Paired', fliersize=1, showfliers = False)\n",
    "plt.savefig('gene2_nonzeroratio_onemillionv2.unfiltered_results.pdf')\n",
    "plt.savefig('gene2_nonzeroratio_onemillionv2.unfiltered_results.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'], \n",
    "            y=annotated_coeqtl_df_clean['eqtlgene_nonzeroratio_onemillionv2'],\n",
    "            hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
    "           palette='Paired', fliersize=1, showfliers = False)\n",
    "plt.savefig('eqtlgene_nonzeroratio_onemillionv2.unfiltered_results.pdf')\n",
    "plt.savefig('eqtlgene_nonzeroratio_onemillionv2.unfiltered_results.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
